In machine learning, artificial neural networks are a family of statistical learning algorithms inspired by biological neural networks, a.k.a., the central nervous systems of animals, in particular the brain. Artificial neural networks are primarily used to estimate or approximate generally unknown functions that can depend on a large number of inputs. Such neural networks have been used for a wide variety of tasks that are difficult to resolve using ordinary rule-based programming, including computer vision and speech recognition.
Artificial neural networks are generally presented as systems of “neurons” which can compute values from inputs, and, as a result of their adaptive nature, are capable of machine learning, as well as pattern recognition. Each neuron frequently connects with several inputs through synapses having synaptic weights.
Neural networks are not programmed as typical software, but are trained. Such training is typically accomplished via analysis of a sufficient number of representative examples and by statistical or algorithmic selection of synaptic weights, so that a given set of input images corresponds to a given set of output images. A common criticism of classical neural networks is that significant time and other resources are frequently required for their training.
Various artificial neural networks are described in the following U.S. Pat. Nos. 4,979,124; 5,479,575; 5,493,688; 5,566,273; 5,682,503; 5,870,729; 7,577,631; and 7,814,038.